关注
Rory Conlin
标题
引用次数
引用次数
年份
Keras2c: A library for converting Keras neural networks to real-time compatible C
R Conlin, K Erickson, J Abbate, E Kolemen
Engineering Applications of Artificial Intelligence 100, 104182, 2021
512021
Data-driven profile prediction for DIII-D
J Abbate, R Conlin, E Kolemen
Nuclear Fusion 61 (4), 046027, 2021
412021
Real-time and adaptive reservoir computing with application to profile prediction in fusion plasma
A Jalalvand, J Abbate, R Conlin, G Verdoolaege, E Kolemen
IEEE Transactions on Neural Networks and Learning Systems 33 (6), 2630-2641, 2021
232021
The DESC stellarator code suite. Part 1. Quick and accurate equilibria computations
D Panici, R Conlin, DW Dudt, K Unalmis, E Kolemen
Journal of Plasma Physics 89 (3), 955890303, 2023
212023
Avoiding fusion plasma tearing instability with deep reinforcement learning
J Seo, SK Kim, A Jalalvand, R Conlin, A Rothstein, J Abbate, K Erickson, ...
Nature 626 (8000), 746-751, 2024
202024
Offline model-based reinforcement learning for tokamak control
I Char, J Abbate, L Bardóczi, M Boyer, Y Chung, R Conlin, K Erickson, ...
Learning for Dynamics and Control Conference, 1357-1372, 2023
182023
The DESC stellarator code suite Part 3: Quasi-symmetry optimization
DW Dudt, R Conlin, D Panici, E Kolemen
Journal of Plasma Physics 89 (2), 955890201, 2023
182023
The DESC stellarator code suite. Part 2. Perturbation and continuation methods
R Conlin, DW Dudt, D Panici, E Kolemen
Journal of Plasma Physics 89 (3), 955890305, 2023
152023
Optimization of nonlinear turbulence in stellarators
P Kim, S Buller, R Conlin, W Dorland, DW Dudt, R Gaur, R Jorge, ...
Journal of Plasma Physics 90 (2), 905900210, 2024
92024
Exploration via planning for information about the optimal trajectory
V Mehta, I Char, J Abbate, R Conlin, M Boyer, S Ermon, J Schneider, ...
Advances in Neural Information Processing Systems 35, 28761-28775, 2022
92022
Multimodal prediction of tearing instabilities in a tokamak
J Seo, R Conlin, A Rothstein, SK Kim, J Abbate, A Jalalvand, E Kolemen
2023 International Joint Conference on Neural Networks (IJCNN), 1-8, 2023
82023
Greedy permanent magnet optimization
AA Kaptanoglu, R Conlin, M Landreman
Nuclear Fusion 63 (3), 036016, 2023
72023
A general infrastructure for data-driven control design and implementation in tokamaks
J Abbate, R Conlin, R Shousha, K Erickson, E Kolemen
Journal of Plasma Physics 89 (1), 895890102, 2023
72023
Avoiding tokamak tearing instability with artificial intelligence
E Kolemen, J Seo, R Conlin, A Rothstein, SK Kim, J Abbate, K Erickson, ...
32023
Magnetic fields with general omnigenity
DW Dudt, AG Goodman, R Conlin, D Panici, E Kolemen
Journal of Plasma Physics 90 (1), 905900120, 2024
22024
Implementation of AI/DEEP learning disruption predictor into a plasma control system
W Tang, G Dong, J Barr, K Erickson, R Conlin, D Boyer, J Kates‐Harbeck, ...
Contributions to Plasma Physics 63 (5-6), e202200095, 2023
12023
Sample-efficient plasma control by planning for optimal trajectory information
V Mehta, I Char, J Schneider, W Neiswanger, S Ermon, J Abbate, ...
ICML2022 Workshop on Adaptive Experimental Design and Active Learning in the …, 2022
12022
An ideal MHD δW stability analysis that bypasses the Newcomb equation
AS Glasser, AH Glasser, R Conlin, E Kolemen
Physics of Plasmas 27 (2), 2020
2020
系统目前无法执行此操作,请稍后再试。
文章 1–18